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Quantifying the Effects of Climate Variability and Change on Hydrologic Extremes in the Pacific Northwest. Alan F. Hamlet JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering University of Washington. CBCCSP Research Team Lara Whitely Binder Pablo Carrasco
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Quantifying the Effects of Climate Variability and Change on Hydrologic Extremes in the Pacific Northwest • Alan F. Hamlet • JISAO/CSES Climate Impacts Group • Dept. of Civil and Environmental Engineering • University of Washington
CBCCSP Research Team Lara Whitely Binder Pablo Carrasco Jeff Deems Marketa McGuire Elsner Alan F. Hamlet Carrie Lee Se-Yeun Lee Dennis P. Lettenmaier Jeremy Littell Guillaume Mauger Nate Mantua Ed Miles Kristian Mickelson Philip W. Mote Rob Norheim Erin Rogers Eric Salathé Amy Snover Ingrid Tohver Andy Wood http://www.hydro.washington.edu/2860/products/sites/r7climate/study_report/CBCCSP_chap1_intro_final.pdf
The Myth of Stationarity: 1) Climate Risks are stationary in time. 2) Observed streamflow records are the best estimate of future variability. 3) Systems and operational paradigms that are robust to past variability are robust to future variability.
The Myth of Stationarity Meets the Death of Stationarity Muir Glacier in Alaska Aug, 13, 1941 Aug, 31, 2004 Image Credit: National Snow and Ice Data Center, W. O. Field, B. F. Molnia http://nsidc.org/data/glacier_photo/special_high_res.html
Why a Focus on Hydrologic Extremes? Many human and natural systems are quite robust under “normal” conditions, but have the potential to be profoundly impacted by hydrologic extreme events.
Drought Evacuated Reservoir During the 2001 PNW Drought
Low Flow and Temperature Impacts to Fish Temperature/ Disease Related Fish Kill in the Klamath River in 2002
Dissolved Gas Management Tailrace below Bonneville Dam
Dam Safety Aftermath of the Johnstown Flood 1889
Nuts and Bolts: Traditional Methods for Estimating Hydrologic Extremes
Step 1: Select Extreme Event from Each Historical Year Streamflow (cfs) Day of the Water Year (1 = Oct 1)
Step 2: Rank Extreme Events for All Years and Estimate Quantiles 1999 Streamflow (cfs) Probability of Exceedance
Step 3: Fit a Probability Distribution to the Data • Examples of Commonly Used Probability Distributions: • Extreme Value Type 1 (EV 1) • Log Normal (LN) • Log Pearson • Generalized Extreme Value (GEV) • For climate change experiments, GEV is a good choice since the true nature of the future probability distributions is essentially unknown. However it turns out that the choice of distribution is not very critical in terms of the evaluating the sensitivity to warming and/or precipitation change.
Step 4: Estimate Extremes Associated with Return Intervals Site Name Ret. Int. Flow (cfs) SNOMO : 20 68660 SNOMO : 50 81332 SNOMO : 100 91145 Note that any return interval can be estimated. E.g. one could provide an estimate of the “5000 year flood”.
Step 5 (Optional) : Regionalize the Results In order to avoid the inherent “noise” that comes with using imperfect site specific data, a common approach is to “regionalize” the results. The idea is to pool as many sites as possible that have common hydroclimatic features (e.g. sites in western WA), and express the flood statistics as a simple ratio to the mean annual flood (MAF) averaged over many different basins. E.g. Q100 = 2.7 * MAF This approach is used by Ecology in providing estimates of extreme events for the Dam Safety Program, for example.
Low flow analysis is essentially the same except we select the extreme low flow event from each year. 7Q10, for example, extracts the lowest 7-day running mean flow from each historical year, fits a probability distribution to the sequence of extremes, and selects the 90% exceedance value (i.e. a 10% probability of being at or below this extreme value)
Historical Perspectives: Changing Flood Risk in the 20th Century
References: Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review) Hamlet AF, Lettenmaier DP (2007) Effects of 20th century warming and climatevariability on flood risk in the western U.S. Water Resour Res, 43:W06427.doi:10.1029/2006WR005099
Role of Atmospheric Rivers in Flooding (Nov 7, 2006) Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review)
Role of Atmospheric Rivers in Flooding (Oct 20, 2003) Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review)
Niemann, PJ, LJ Schick, FM Ralph, M Hughes, GA Wick, 2010: Flooding in Western Washington: The Connection to Atmospheric Rivers, J. of Hydrometeorology, (in review)
Schematic of VIC Hydrologic Model • Sophisticated, fully distributed, physically based hydrologic model • Widely used globally in climate change applications • 1/16 Degree Resolution (~5km x 6km or ~ 3mi x 4mi) General Model Schematic Snow Model
Evaluating the Hydrologic Model Simulations in the Context of Reproducing Flood Characteristics Ln (X100 / Xmean) OBS Avg WY Date of Flooding OBS Avg WY Date of Flooding VIC Ln (X100 / Xmean) VIC Red = PNW, Blue = CA, Green = Colo, Black = GB
100-yr Red = VIC Blue = OBS 50-yr X100 GEV flood/mean flood 20-yr 10-yr 5-yr Zp
Regionally Averaged Temperature Trends Over the Western U.S. 1916-2003 Tmax PNW GB Tmin CA CRB
Detrended Temperature Driving Data for Flood Risk Experiments “Pivot 2003” Data Set Temperature Historic temperature trend in each calendar month “Pivot 1915” Data Set 2003 1915
Simulated Changes in the 20-year Flood Associated with 20th Century Warming DJF Avg Temp (C) X20 2003 / X20 1915 DJF Avg Temp (C) X20 2003 / X20 1915 X20 2003 / X20 1915
Schematic of a Cool Climate Flood Precipitation Produces Runoff Precipitation Produces Snow Precipitation Produces Snow Snow Snow Freezing Level Snow Melt
Schematic of a Warm Climate Flood Precipitation Produces Runoff Precipitation Produces Snow Precipitation Produces Snow Snow Snow Snow Melt Freezing Level
20-year Flood for “1973-2003” Compared to “1916-2003” for a Constant Late 20th Century Temperature Regime DJF Avg Temp (C) X20 ’73-’03 / X20 ’16-’03 X20 ’73-’03 / X20 ’16-’03
Summary of Flooding Impacts Rain Dominant Basins: Increases in flooding due to increased precipitation intensity, but no significant change from warming alone. Mixed Rain and Snow Basins Along the Coast: Strong increases due to warming and increased precipitation intensity (both effects increase flood risk) Inland Snowmelt Dominant Basins: Relatively small overall changes because effects of warming (decreased risks) and increased precipitation intensity (increased risks) are typically in the opposite directions.
X100 wENSO / X100 2003 X100 nENSO / X100 2003 X100 cENSO / X100 2003 DJF Avg Temp (C) DJF Avg Temp (C) DJF Avg Temp (C) X100 wENSO / X100 2003 X100 nENSO / X100 2003 X100 cENSO / X100 2003
X100 wPDO / X100 2003 X100 nPDO / X100 2003 X100 cPDO / X100 2003 DJF Avg Temp (C) DJF Avg Temp (C) DJF Avg Temp (C) X100 wPDO / X100 2003 X100 nPDO / X100 2003 X100 cPDO / X100 2003
21st Century Climate Impacts for the Pacific Northwest Region Mote, P.W. and E. P. Salathe Jr., 2009: Future climate in the Pacific Northwest
Seasonal Precipitation Changes for the Pacific Northwest http://cses.washington.edu/db/pdf/wacciach1scenarios642.pdf
HumanHealth Infrastructure Water Resources Agriculture/Economics A comprehensive climate change impacts assessment for Washington State Coasts Energy Forest Resources Salmon Adaptation
The Columbia Basin Climate Change Scenarios Project 297 Streamflow Sites This 3-year research project was designed to provide a comprehensive suite of 21st century hydroclimatological scenarios for the Columbia River basin and coastal drainages in OR and WA. Collaborative Partners: • WA State Dept. of Ecology (via HB 2860) • Bonneville Power Administration • Northwest Power and Conservation Council • Oregon Water Resources Department • BC Ministry of the Environment
Columbia Basin Climate Change Scenarios Project 297 Sites • Smaller basins down to • ~500 km2 • Monthly and daily streamflow time series • Assessment of hydrologic extremes • (e.g. Q100 and 7Q10)
Available PNW Scenarios 2020s – mean 2010-2039; 2040s – mean 2030-2059; 2080s – mean 2070-2099